AVP AI QA Engineer

State Street

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Burlington, MA
Salary
$80,000–$140,000 / yr
Posted
73 days ago
Freshness
Confirmed live yesterday
Closes
Sep 29, 2026

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $176k
This role $110k
$64k most similar roles pay here $229k

This role pays less than 97% of similar roles. Most pay $144,756–$206,737 — the shaded band above. At the midpoint, this role pays about $110k versus about $176k for comparable roles.

Based on 240 similar postings.

Employer

About State Street

State Street Corporation is one of the world''s largest custodian banks and asset managers, providing investment servicing, investment management, and investment research to institutional investors. Industry: Financial Services & Asset Custody

State Street currently has 176 open roles on FindRole.

Listed pay typically runs $120,000–$202,500 across 172 roles with salary data.

Most-posted roles

View all roles at State Street

At a glance

TL;DR · AVP AI QA Engineer

As an AI QA Engineer, AVP, you will join the team responsible for advancing quality engineering for AI-enabled capabilities within the CRIMS and State Street Alpha product suite. Working within a scrum team, you will design and implement scalable test frameworks to validate AI-driven behaviors such as model-integrated workflows, intelligent recommendations, and generated outputs. You will develop automated and manual test plans for functional, integration, regression, and non-functional testing while ensuring accuracy, consistency, and robustness in probabilistic results. Your role involves managing the defect lifecycle, integrating tests into CI/CD pipelines, and monitoring production drift. Required skills include 5–8 years of experience in software testing, proficiency in API and end-to-end validation, and expertise in AI-specific concepts like guardrail testing. You will utilize scripting and automation tools to ensure high-quality performance for complex enterprise investment management technology solutions.

What you'll do

  • Design and build reusable QA frameworks specifically tailored to validate AI-driven functionality and model-integrated workflows.
  • Develop test plans, cases, and data strategies for functional, integration, regression, and non-functional testing of AI capabilities.
  • Validate AI-specific quality dimensions including consistency, accuracy, robustness, explainability, and edge-case handling.
  • Execute tests for AI guardrails, probabilistic outputs, and response boundaries to ensure safety and alignment with business goals.
  • Monitor AI behavior in production to detect drift, track output quality trends, and manage feedback loops.
  • Integrate automated testing into CI/CD pipelines to improve test coverage across the development lifecycle.
  • Apply AI tools and techniques within the testing process to accelerate coverage creation and defect discovery.
  • Identify, document, and drive the resolution of defects by analyzing root cause patterns and business impact.

What we're looking for

  • 5–8 years of professional software testing or quality assurance experience, preferably within agile or scrum-based teams.
  • Proven experience implementing AI capabilities within the software testing process for test generation, optimization, or defect analysis.
  • Hands-on experience testing AI functionality embedded in products, including validation of generated outputs and model-driven behavior.
  • Understanding of AI-specific testing concepts such as guardrail testing, probabilistic output assessment, and monitoring for production drift.
  • Strong knowledge of QA methodologies, test strategy development, defect lifecycle management, and automation best practices.
  • Experience building or extending test frameworks to support scalable, maintainable, and reusable testing practices.
  • Proficiency in API testing, integration testing, regression testing, and CI/CD pipeline integration.
  • Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.

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